Bayesian Heterogeneity Modeling

Bayesian Heterogeneity Modeling is a statistical framework within Bayesian meta-analysis that characterizes between-study variability through probabilistic modeling of random effects, distinguishing itself by incorporating prior distributions on heterogeneity parameters to quantify uncertainty in effect-size variation across studies.

71

Publications

8.5K

Citations

175

Authors

116

Institutions

Publications per year

2017–2026

32

Authors

175

  1. JP
    Julian P. T. Higgins

    University of Bristol, United Kingdom

    8 publications · 5.9K citations

    8

    h-index

  2. RT
    Rebecca Turner

    MRC Biostatistics Unit, United Kingdom

    6 publications · 2K citations

    6

    h-index

  3. HC
    Haitao Chu

    University of Minnesota, United States

    5 publications · 76 citations

    5

    h-index

  4. SG
    Simon G. Thompson

    University of Cambridge, United Kingdom

    4 publications · 3.8K citations

    4

    h-index

  5. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    3 publications · 232 citations

    3

    h-index

1–5 of 175

Venues

  1. Statistics in Medicine

    16 publications · 1.8K citations

    13

    h-index

  2. Statistical Methods in Medical Research

    4 publications · 97 citations

    4

    h-index

  3. 2

    h-index

  4. Crop Science

    2 publications · 73 citations

    2

    h-index

  5. 2

    h-index

1–5